A linear programming based heuristic framework for min-max regret combinatorial optimization problems with interval costs

This work deals with a class of problems under interval data uncertainty, namely interval robust-hard problems, composed of interval data min-max regret generalizations of classical NP-hard combinatorial problems modeled as 0-1 integer linear programming problems. These problems are more challenging than other interval data min-max regret problems, as solely computing the cost of any feasible solution requires solving an instance of an NP-hard problem. The state-of-the-art exact algorithms in the literature are based on the generation of a possibly exponential number of cuts. As each cut separation involves the resolution of an NP-hard classical optimization problem, the size of the instances that can be solved efficiently is relatively small. To smooth this issue, we present a modeling technique for interval robust-hard problems in the context of a heuristic framework. The heuristic obtains feasible solutions by exploring dual information of a linearly relaxed model associated with the classical optimization problem counterpart. Computational experiments for interval data min-max regret versions of the restricted shortest path problem and the set covering problem show that our heuristic is able to find optimal or near-optimal solutions and also improves the primal bounds obtained by a state-of-the-art exact algorithm and a 2-approximation procedure for interval data min-max regret problems.     

A continuous optimization framework for hybrid system identification

We propose a new framework for hybrid system identification, which relies on continuous optimization. This framework is based on the minimization of a cost function that can be chosen as either the minimum or the product of loss functions. The former is inspired by traditional estimation methods, while the latter is inspired by recent algebraic and support vector regression approaches to hybrid system identification. In both cases, the identification problem is recast as a continuous optimization program involving only the real parameters of the model as variables, thus avoiding the use of discrete optimization. This program can be solved efficiently by using standard optimization methods even for very large data sets. In addition, the proposed framework easily incorporates robustness to different kinds of outliers through the choice of the loss function.     

A novel optimization framework for salient object detection

Visual saliency aims to locate the noticeable regions or objects in an image. In this paper, a coarse-to-fine measure is developed to model visual saliency. In the proposed approach, we firstly use the contrast and center bias to generate an initial prior map. Then, we weight the initial prior map with boundary contrast to obtain the coarse saliency map. Finally, a novel optimization framework that combines the coarse saliency map, the boundary contrast and the smoothness prior is introduced with the intention of refining the map. Experiments on three public datasets demonstrate the effectiveness of the proposed method.     

A hyper-heuristic based framework for dynamic optimization problems

Most of the real world problems have dynamic characteristics, where one or more elements of the underlying model for a given problem including the objective, constraints or even environmental parameters may change over time. Hyper-heuristics are problem-independent meta-heuristic techniques that are automating the process of selecting and generating multiple low-level heuristics to solve static combinatorial optimization problems. In this paper, we present a novel hybrid strategy for applicability of hyper-heuristic techniques on dynamic environments by integrating them with the memory/search algorithm. The memory/search algorithm is an important evolutionary technique that have applied on various dynamic optimization problems. We validate performance of our method by considering both the dynamic generalized assignment problem and the moving peaks benchmark. The former problem is extended from the generalized assignment problem by changing resource consumptions, capacity constraints and costs of jobs over time; and the latter one is a well-known synthetic problem that generates and updates a multidimensional landscape consisting of several peaks. Experimental evaluation performed on various instances of the given two problems validates that our hyper-heuristic integrated framework significantly outperforms the memory/search algorithm.     

A formal framework for evaluating heuristic programs

We address the question of how one evaluates the usefulness of a heuristic program on a particular input. If theoretical tools do not allow us to decide for every instance whether a particular heuristic is fast enough, might we at least write a simple, fast companion program that makes this decision on some inputs of interest? We call such a companion program a timer for the heuristic. Timers are related to program checkers, as defined by Blum (1993), in the following sense: Checkers are companion programs that check the correctness of the output produced by (unproven but bounded‐time) programs on particular instances; timers, on the other hand, are companion programs that attempt to bound the running time on particular instances of correct programs whose running times have not been fully analyzed. This paper provides a family of definitions that formalize the notion of a timer and some preliminary results that demonstrate the utility of these definitions.     

A novel computational framework for structural optimization with patched laminates

Structural and Multidisciplinary Optimization - Fiber patch placement (FPP) is a manufacturing technique for discrete variable stiffness composites. In the FPP approach, a structural component is...     

A novel two-stage constraints handling framework for real-world multi-constrained multi-objective optimization problem based on evolutionary algorithm

Applied Intelligence - Multi-constrained multi-objective optimization is a challenging topic, which is very common in dealing with real-world problems. This paper proposes a novel two-stage ρg...     

A combined heuristic optimization technique

Realistic problems of structural optimization are characterized by non-linearity, non-convexity and by continuous and/or discrete design variables. There are non-linear dependencies between the optimised parameters. Real-world problems are rarely decomposable or separable. In this contribution a combined heuristic algorithm is described which is well suited for problems, for which the application-requirements of gradient-based algorithms are not fulfilled. The present contribution describes a combination of the Threshold Accepting Algorithm with Differential Evolution with particular emphasis on structural optimization, it can be classified as a Hybrid Evolutionary Algorithm. The Threshold Accepting Algorithm is similar to Simulated Annealing. Differential Evolution is based on Genetic Algorithms.     

A novel framework for UAV returning based on FPGA

The Journal of Supercomputing - To date, most unmanned aerial vehicle (UAV) returning technology has relied on the global positioning system (GPS). The risk is that the UAV may be spoofed by fake...     

A new heuristic approach for non-convex optimization problems

In this work a new optimization method, called the heuristic Kalman algorithm (HKA), is presented. This new algorithm is proposed as an alternative approach for solving continuous, non-convex optimization problems. The principle of HKA is to explicitly consider the optimization problem as a measurement process designed to give an estimate of the optimum. A specific procedure, based on the Kalman estimator, was developed to improve the quality of the estimate obtained through the measurement process. The main advantage of HKA, compared to other metaheuristics, lies in the small number of parameters that need to be set by the user. Further, it is shown that HKA converges almost surely to a near-optimal solution. The efficiency of HKA was evaluated in detail using several non-convex test problems, both in the unconstrained and constrained cases. The results were then compared to those obtained via other metaheuristics. The numerical experiments show that HKA is a promising approach for solving non-convex optimization problems, particularly in terms of computation time and success ratio.     

Adaptive heuristic search algorithm for discrete variables based multi-objective optimization

Although metamodel technique has been successfully used to enhance the efficiency of the multi-objective optimization (MOO) with black-box objective functions, the metamodel could become less accurate or even unavailable when the design variables are discrete. In order to overcome the bottleneck, this work proposes a novel random search algorithm for discrete variables based multi-objective optimization with black-box functions, named as k-mean cluster based heuristic sampling with Utopia-Pareto directing adaptive strategy (KCHS-UPDA). This method constructs a few adaptive sampling sets in the solution space and draws samples according to a heuristic probability model. Several benchmark problems are supplied to test the performance of KCHS-UPDA including closeness, diversity, efficiency and robustness. It is verified that KCHS-UPDA can generally converge to the Pareto frontier with a small quantity of number of function evaluations. Finally, a vehicle frontal member crashworthiness optimization is successfully solved by KCHS-UPDA.     

A bilevel framework for joint optimization of session compensation and classification for speaker identification

The i-vector framework based system is one of the most popular systems in speaker identification (SID). In this system, session compensation is usually employed first and then the classifier. For any session-compensated representation of i-vector, there is a corresponding identification result, so that both the stages are related. However, in current SID systems, session compensation and classifier are usually optimized independently. An incomplete knowledge about the session compensation to the identification task may lead to involving uncertainties. In this paper, we propose a bilevel framework to jointly optimize session compensation and classifier to enhance the relationship between the two stages. In this framework, we use the sparse coding (SC) to obtain the session-compensated feature by learning an overcomplete dictionary, and employ the softmax classifier and support vector machine (SVM) in classifying respectively. Moreover, we present a joint optimization of the dictionary and classifier parameters under a discriminative criterion for classifier with conditions for SC. In addition, the proposed methods are evaluated on the King-ASR-010, VoxCeleb and RSR2015 databases. Compared with typical session compensation techniques, such as linear discriminant analysis (LDA) and nonparametric discriminant analysis (NDA), our methods can be more robust to complex session variability. Moreover, compared with the typical classifiers in i-vector framework, i.e. the cosine distance scoring (CDS) and probabilistic linear discriminant analysis (PLDA), our methods can be more suitable for SID (multiclass task).     

数据仓库索引启发式查询优化方法

在大型数据仓库查询过程中,经常涉及多事实表的连接操作。传统的查询优化方法是在计算多关系连接时尽可能地减少中间关系的大小,并没有考虑到数据仓库中数据的海量,以读为主且事实表一般建有索引的特点,往往无法取得最优的效果。针对数据仓库查询的特点,提出了一种利用索引加快查询的启发式优化方法。理论分析与实验表明,该方法在查询处理代价和执行时间上都明显减少,方法具有有效性。     

Decomposition methodology for classification tasks: a meta decomposer framework

The idea of decomposition methodology for classification tasks is to break down a complex classification task into several simpler and more manageable sub-tasks that are solvable by using existing induction methods, then joining their solutions together in order to solve the original problem. In this paper we provide an overview of very popular but diverse decomposition methods and introduce a related taxonomy to categorize them. Subsequently, we suggest using this taxonomy to create a novel meta-decomposer framework to automatically select the appropriate decomposition method for a given problem. The experimental study validates the effectiveness of the proposed meta-decomposer on a set of benchmark datasets.     

jMetal: A Java framework for multi-objective optimization

This paper describes jMetal, an object-oriented Java-based framework aimed at the development, experimentation, and study of metaheuristics for solving multi-objective optimization problems. jMetal includes a number of classic and modern state-of-the-art optimizers, a wide set of benchmark problems, and a set of well-known quality indicators to assess the performance of the algorithms. The framework also provides support to carry out full experimental studies, which can be configured and executed by using jMetal’s graphical interface. Other features include the automatic generation of statistical information of the obtained results, and taking advantage of the current availability of multi-core processors to speed-up the running time of the experiments. In this work, we include two case studies to illustrate the use of jMetal in both solving a problem with a metaheuristic and designing and performing an experimental study.     

A hybrid meta heuristic algorithm for bi-objective minimum cost flow (BMCF) problem

In this paper we study the bi-objective minimum cost flow (BMCF) problem which can be categorized as multi objective minimum cost flow problems. Generally, the exact computation of the efficient frontier is intractable and there may exist an exponential number of extreme non-dominated objective vectors. Hence, it is better to employ an approximate method to compute solutions within reasonable time. Therefore, we propose a hybrid meta heuristic algorithm (memetic algorithm hybridized with simulated annealing MA/SA) to develop an efficient approach for solving this problem. In order to show the efficiency of the proposed MA/SA some problems have been generated and solved by both the MA/SA and an exact method. It is perceived from this evaluation that the proposed MA/SA outputs are very close to the exact solutions. It is shown that when the number of arcs and nodes exceed 30 (large problems) the MA/SA model will be more preferred because of its strongly shorter computational time in comparison with exact methods.     

A novel framework for software defined based secure storage systems

The Software Defined Systems (SDSys) paradigm has been introduced recently as a solution to reduce the overhead in the control and management operations of complex computing systems and to maintain a high level of security and protection. The main concept behind this technology is around isolating the data plane from the control plane. Building a Software Defined System in a real life environment is considered an expensive solution and may have a lot of risks. Thus, there is a need to simulate such systems before the real-life implementation and deployment. In this paper we present a novel experimental framework as a virtualized testbed environment for software defined based secure storage systems. Its also covers some related issues for large scale data storage and sharing such as deduplication. This work builds on the Mininet simulator, where its core components, the host, switch and the controller, are customized to build the proposed experimental simulation framework. The developed emulator, will not only support the development and testing of SD-based secure storage solutions, it will also serve as an experimentation tool for researchers and for benchmarking purposes. The developed simulator/emulator could also be used as an educational tool to train students and novice researchers.     

A heuristic optimization algorithm for HMM based on SA and EM in machinery diagnosis

This paper proposes a novel hidden Markov model (HMM) based on simulated annealing (SA) algorithm and expectation maximization (EM) algorithm for machinery diagnosis. As traditional HMM is sensitive to initial values and EM is easy to trap into a local optimization, SA is combined to improve HMM which can overcome local optimization searching problem. The proposed HMM has strong ability of global convergence, and optimizes the process of parameters estimation. Finally, through a case study, the computation results illustrate this SAEM-HMM has high efficiency and accuracy, which could help machinery diagnosis in practical.     

A framework for self-tuning optimization algorithm

The performance of any algorithm will largely depend on the setting of its algorithm-dependent parameters. The optimal setting should allow the algorithm to achieve the best performance for solving a range of optimization problems. However, such parameter tuning itself is a tough optimization problem. In this paper, we present a framework for self-tuning algorithms so that an algorithm to be tuned can be used to tune the algorithm itself. Using the firefly algorithm as an example, we show that this framework works well. It is also found that different parameters may have different sensitivities and thus require different degrees of tuning. Parameters with high sensitivities require fine-tuning to achieve optimality.     

A syllable-scale framework for language identification

Whilst several examples of segment based approaches to language identification (LID) have been published, they have been typically conducted using only a small number of languages, or varying feature sets, thus making it difficult to determine how the segment length influences the accuracy of LID systems. In this study, phone-triplets are used as crude approximates for a syllable-length sub-word segmental unit. The proposed pseudo-syllabic length framework is subsequently used for both qualitative and quantitative examination of the contributions made by acoustic, phonotactic and prosodic information sources, and trialled in accordance with the NIST 1996 LID protocol. Firstly, a series of experimental comparisons are conducted which examine the utility of using segmental units for modelling short term acoustic features. These include comparisons between language specific Gaussian mixture models (GMMs), language specific GMMs for each segmental unit, and finally language specific hidden Markov models (HMM) for each segment, undertaken in an attempt to better model the temporal evolution of acoustic features. In a second tier of experiments, the contribution of both broad and fine class phonotactic information, when considered over an extended time frame, is contrasted with an implementation of the currently popular parallel phone recognition language modelling (PPRLM) technique. Results indicate that this information can be used to complement existing PPRLM systems to obtain improved performance. The pseudo-syllabic framework is also used to model prosodic dynamics and compared to an implemented version of a recently published system, achieving comparable levels of performance.